code: def test_multiobjective_improvement():
      # input data initialization
      train_data_path = fedot_project_root().joinpath(
          "cases/data/scoring/scoring_train.csv"
      )
      test_data_path = fedot_project_root().joinpath(
          "cases/data/scoring/scoring_test.csv"
      )
      problem = "classification"
      seed = 50
  
      # Define parameters for composing
      quality_metric_1 = "roc_auc"
      quality_metric_2 = "accuracy"
      metrics = [quality_metric_1, quality_metric_2]
  
      timeout = 4
      composer_params = dict(
          num_of_generations=10,
          pop_size=10,
          with_tuning=False,
          preset="fast_train",
          metric=metrics,
      )
  
      auto_model = Fedot(
          problem=problem,
          timeout=timeout,
          seed=seed,
          logging_level=logging.DEBUG,
          **composer_params,
          use_pipelines_cache=False,
          use_preprocessing_cache=False,
      )
      auto_model.fit(features=train_data_path, target="target")
      auto_model.predict_proba(features=test_data_path)
      auto_metrics = auto_model.get_metrics()
  
      quality_1_improved, quality_2_improved = check_improvement(
          auto_model.history
      )
  
      assert auto_metrics[quality_metric_1] > 0.75
      assert auto_metrics[quality_metric_2] > 0.75
      assert quality_1_improved
      assert quality_2_improved
  
file_path: FEDOT/test/integration/quality/test_quality_improvement.py
imports: import logging
  import warnings
  from typing import Sequence
  import numpy
  from fedot import Fedot
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 2 for calling a global function with example usage: test_multiobjective_improvement() without return types